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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer
Qingpei Lai1, Xinzhi Teng1, Jiang Zhang1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Abstract:
This study addresses the limited mechanistic understanding behind medical imaging for tumor microenvironment (TME) assessment. We developed a novel framework that analyzes tumor imaging heterogeneity index (TIHI)-correlated genes to uncover underlying TME biology and therapeutic vulnerabilities. DCE-MRI and mRNA data from 987 high-risk breast cancer patients in the I-SPY2 trial, together with mRNA data from 508 patients in GSE25066, were analyzed. TIHI-associated genes were identified via Pearson correlation, clustered via weighted gene co-expression network analysis (WGCNA), and subgroups were defined via non-negative matrix factorization (NMF). The clinical relevance of the image-to-gene comprehensive (I2G-C) subtype defined by subgroups was assessed using logistic regression and Cox analysis. I2G-C comprised four clusters with distinct immune and replication/repair functions. It further stratified receptor, PAM50, and RPS5 subtypes. The "immune+/replication+" was more likely to achieve pathological complete response (pCR) (OR = 2.587, p < 0.001), while the "immune-/replication-" was the least likely to achieve pCR (OR = 0.402, p < 0.001). The "immune+/replication+" showed sensitivity to pembrolizumab (OR = 10.192, p < 0.001) and veliparib/carboplatin (OR = 5.184, p = 0.006), while "immune-/replication-" responded poorly to pembrolizumab (OR = 0.086, p < 0.001). Additionally, "immune+/replication-" had the best distant recurrence-free survival (DRFS), whereas "immune-/replication+" had the worst (log-rank p = 6 × 10-4, HR = 5.45). By linking imaging heterogeneity directly to molecular subtypes and therapeutic response, this framework provides a robust, non-invasive surrogate for genomic profiling and a strategic tool for personalized neoadjuvant therapy selection.
Insights
This study links medical imaging heterogeneity to tumor biology and treatment response in breast cancer. A novel framework reveals subtypes predicting pathological complete response and survival, guiding personalized neoadjuvant therapy.
Area of Science:
- Oncology
- Medical Imaging
- Genomics
- Translational Research
Background:
- Limited mechanistic understanding of medical imaging for tumor microenvironment (TME) assessment hinders personalized treatment.
- Tumor imaging heterogeneity index (TIHI) offers potential insights but requires biological correlation.
- Integrating imaging data with molecular profiling is crucial for advancing breast cancer therapy.
Purpose of the Study:
- To develop a novel framework analyzing TIHI-correlated genes for uncovering TME biology and therapeutic vulnerabilities.
- To define image-to-gene comprehensive (I2G-C) subtypes and assess their clinical relevance in high-risk breast cancer.
- To stratify patients based on imaging heterogeneity for improved neoadjuvant therapy selection.
Main Methods:
- Analysis of DCE-MRI and mRNA data from 987 high-risk breast cancer patients (I-SPY2 trial) and 508 patients (GSE25066).
- Identification of TIHI-associated genes using Pearson correlation, followed by clustering with Weighted Gene Co-expression Network Analysis (WGCNA).
- Subgroup definition via Non-negative Matrix Factorization (NMF), and clinical relevance assessment using logistic regression and Cox analysis.
Main Results:
- Four I2G-C clusters with distinct immune and replication/repair functions were identified, stratifying known molecular subtypes.
- The "immune+/replication+" subtype showed significantly higher pathological complete response (pCR) rates (OR=2.587) and sensitivity to pembrolizumab (OR=10.192) and veliparib/carboplatin (OR=5.184).
- The "immune-/replication-" subtype had the lowest pCR rates (OR=0.402) and poor response to pembrolizumab (OR=0.086), while survival varied significantly across subtypes.
Conclusions:
- The developed framework effectively links imaging heterogeneity to molecular subtypes and therapeutic response in breast cancer.
- I2G-C subtypes serve as a robust, non-invasive surrogate for genomic profiling, aiding in personalized neoadjuvant therapy selection.
- This approach offers a strategic tool for optimizing treatment strategies based on individual tumor characteristics.
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